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Angiotensin-converting enzyme (ACE), a vital component of the renin-angiotensin-aldosterone system, is abundant in lung endothelial cells. ACE converts the inactive decapeptide, angiotensin I, into the active octapeptide, angiotensin II. This potent vasoconstrictor narrows blood vessels, increasing resistance to blood flow and elevating blood pressure. Angiotensin II also stimulates aldosterone production, encouraging kidney cells to reabsorb more sodium and water from urine, thereby increasing...
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Machine Learning Approaches to Investigate the Structure-Activity Relationship of Angiotensin-Converting Enzyme

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This study optimizes angiotensin-converting enzyme inhibitors (ACEIs) using machine learning to improve drug discovery. Findings reveal scaffold diversity is key for active ACEIs, guiding future lead optimization efforts.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Angiotensin-converting enzyme inhibitors (ACEIs) are vital for treating hypertension, heart failure, and kidney diseases.
  • Current ACEIs have adverse effects, notably renal insufficiency, necessitating optimization.
  • There is a significant need for improved ACEI therapies with better safety profiles.

Purpose of the Study:

  • To conduct a structure-activity relationship (SAR) investigation of ACEIs using machine learning.
  • To identify key structural features and scaffolds that contribute to ACEI activity and reduce adverse effects.
  • To develop predictive quantitative structure-activity relationship (QSAR) models for novel ACEI design.

Main Methods:

  • Utilized machine learning algorithms to analyze ACEI data from the ChEMBL database.
  • Performed exploratory data analysis and scaffold analysis to identify significant Murcko scaffolds.
  • Developed and validated QSAR models using Mordred descriptors and Random Forest/Extreme Gradient Boost algorithms.

Main Results:

  • Identified 9 representative Murcko scaffolds, with active ACEIs showing greater scaffold diversity.
  • Scaffolds 2, 3, 5, 7, and 9 were found to be more favorable than scaffolds 1, 3, 6, and 8.
  • Developed robust QSAR models with high accuracy (up to 0.981) and MCC (up to 0.972) for predicting ACEI activity.
  • Identified activity cliffs through SALI plot analysis, highlighting specific structural variations impacting efficacy.

Conclusions:

  • Scaffold diversity is crucial for optimizing ACEI activity and reducing side effects.
  • Machine learning and QSAR modeling provide powerful tools for advancing ACEI drug discovery.
  • The identified favorable scaffolds and SAR insights can guide the design of next-generation ACEIs.